How to Actually Use AI and MCPs for Game Backend Development

AI coding tools are popping up everywhere, and most game developers have had the same experience: you point the tool at something real, like a service flow or backend action, and suddenly it's guessing, mixing up types, or touching files it had no business touching.

Fun to play with. Not fun when your game relies on this stuff actually working.

We hit those same walls. So our engineering team figured out how to make AI genuinely useful for game backend development. That led to an internal crash course from our Principal Engineer, Anggoro Dewanto, on using AI agents and MCPs in day-to-day backend workflows.

The session is packed with info, but it's also 40 minutes long so we’ll cover the key takeaways in this post:

• Why AI coding feels unreliable out of the box
• How MCPs help AI agents behave in a predictable way
• A lightweight workflow you can try in your own backend projects
• How we use AI Agents + MCPs at AccelByte
• Guardrails to keep your repo and environment safe

Why AI coding feels unreliable out of the box

AI models feel unpredictable for one simple reason. At their core, they are just autocomplete on steroids. 

• LLMs are predictive, not intelligent; they forecast the next token based on immediate context, not via reasoning or memory. 
• This prediction-based nature causes "hallucinations" (incorrect types, missing functions, etc.) when the context is unclear. 
• Crucially, AI models lack state retention; they forget past interactions unless the full context is provided each time, leading to "muddy" long sessions and confusion. 
• Even with tools that inject hidden context (like Cursor), explicit structure and guardrails remain essential to prevent the AI from wandering.

How MCPs help AI agents behave in a predictable way

Before we talk about predictability, here is what an AI agent and a Model Context Protocol actually are:

Here is how MCP makes AI way more predictable for backend work:

• • “What endpoints exist?”
  • “What parameters do they take?”
  • “How do I call them?”

• The AI agent operates within clear boundaries, only making calls to the endpoints exposed by the MCP server.
• This structured environment eliminates much of the guesswork associated with inventing process flows.

3. It keeps the AI honest:

• If the AI tries to call something that does not exist, MCP tells it.
• If an endpoint requires certain params, MCP enforces it.
• No more made-up URLs or missing fields.

4. It turns the AI from a “guesser” into a tool user:

Instead of predicting code blindly, the agent:

• inspect your backend
• fetch actual data
• read schemas
• make real calls (read-only or write, depending on your setup)

5. Predictability comes from grounding:

When AI has real context, real endpoints, and clear boundaries, it stops hallucinating and behaves like a junior developer who follows instructions.

How to Work With AI Agents Effectively

Once MCP gives the AI a stable foundation, the next step is understanding how to actually work with an AI agent in a real backend project. This is where a lot of teams go off the rails. Dewa breaks this down into three simple ideas that work together to keep the AI predictable and useful instead of chaotic.

1. The levels of help you can expect from AI 

It helps to think of AI adoption as a ladder, not a jump. You start simple and move up: 

• Autocomplete Assist: The model fills in code like an upgraded Copilot.
• AI-as-Editor: You ask it to refactor, add logs, or write tests and then review the results.
• AI-as-Build-Partner: You plan a feature with the model first, then let it execute.
• Spec-Driven Implementation: You give design and tech spec, and agent builds the feature.
• Automated Ticket to PR: The AI handles entire tasks and submits PRs you simply review.

Most teams float between level 2 and 4. Anything higher only works when you have guardrails.

2. The roles you still play as the human in the loop

AI does not replace developers. It shifts your responsibilities:

• Product Manager: Write clear feature descriptions with no hidden assumptions.
• Tech Lead: Review architecture choices and code the agent produces.
• QA Engineer: Ask the AI to write tests and use those tests to catch mistakes.
• Project Manager: Break tasks down, limit scope, and keep things documented.
  
  If you skip any of these roles, the AI fills the gaps with guesses. That is when things go wrong.

These four lightweight docs give the AI the context it needs to behave:

• Feature / Design Spec: Plain-English explanation of what the feature should do.
• Technical Spec: Endpoints, data models, flows, and any architectural notes.
• Status Doc: A running log of what has been done, what is next, and which spec is active.
• Agent Steering Doc:  Rules for the AI. Things like “do not write code unless asked,” “update the status doc every time,” or “use MCP for AGS calls.”

This system prevents hallucinations, keeps long sessions stable, and anchors the AI’s decisions.

How these three ideas come together

• The levels set how much work you trust the AI with.
• Your roles define what guidance the AI receives.
• The docs provide the context and guardrails the AI depends on.

How to try this in your backend

Once you understand how to guide an AI agent and what guardrails it needs, the actual workflow becomes surprisingly lightweight. This is the same approach we use when building backend features with AI + MCP, and it maps well to any studio workflow.

How AccelByte’s MCP Servers Help AI Build Real Backend Features

During the crash course, Dewa gave the AI a real task to build a small backend feature called the Daily Login Challenge. It tracks when players log in, counts their streak, and rewards them for consistency.

A simple enough feature, but a great test for seeing whether AI can actually build something useful instead of just generating snippets. To pull it off, Dewa connected his AI agent to two MCP servers built at AccelByte:

1. The Extend MCP Server

Helps the AI agent write code using AccelByte SDKs. This is purely for coding assistance. So when the AI started building the Daily Login Challenge, it wasn’t inventing files or naming things at random, it was following a proven layout:

• discover which endpoints existed
• check what parameters were required (like player ID, namespace, reward type)

Basically, it learned how to build like a real engineer instead of a text generator.

2.  The AGS API MCP Server

The second MCP server gave the AI real access to backend APIs, not by calling production systems directly, but by letting it inspect how the APIs actually work. This meant the AI could:

• Call the endpoints on your behalf so that you don't need to open Admin Portal

So when the AI agent had to update a player’s login streak or grant a reward, it didn’t make up a fake API call, it used verified, real endpoints exposed through the MCP.

With both MCP servers plugged in:

• The Extend MCP kept the AI’s code organized and consistent
• The AccelByte MCP grounded its logic in real backend data
• Together, they helped the AI agent build and test a working Daily Login Challenge service without breaking anything

The end result wasn’t magic, it was structure and context. That’s what makes AI actually useful for backend work: not bigger models, but better grounding.

Try This Yourself

If you want to experiment with this workflow in your own projects, you can start today.

The MCP servers mentioned are now available as public repositories on AccelByte’s GitHub, so you can see exactly how they work and wire them into your setup.

Whether you’re prototyping a new service, automating repetitive work, or exploring AI-assisted development more seriously, these MCPs give you a practical, grounded way to move beyond code guessing and start building against real backend systems.